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497c818 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 | """Minimal extracted datasets for single-control and three-control training."""
from __future__ import annotations
import json
import os
import random
from pathlib import Path
from typing import Optional, Sequence
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageFile
from torch.utils.data import Dataset
from torchvision.transforms import CenterCrop, Normalize, Resize
from torchvision.transforms.functional import to_tensor
ImageFile.LOAD_TRUNCATED_IMAGES = False
IMAGE_EXTS = (".jpg", ".jpeg", ".JPG", ".JPEG", ".png", ".PNG")
DEPTH_EXTS = (".depth.npy", ".npy", ".depth.png", ".png", ".depth.jpg", ".jpg", ".depth.jpeg", ".jpeg")
SEG_EXTS = (".sam2_label.npy", ".sam2_label.png", ".png", ".npy")
EDGE_EXTS = (".edge.npy", ".npy", ".edge.png", ".png", ".edge.jpg", ".jpg", ".edge.jpeg", ".jpeg")
def strip_image_ext(filename: str) -> str:
for ext in IMAGE_EXTS:
if filename.endswith(ext):
return filename[: -len(ext)]
return os.path.splitext(filename)[0]
def find_with_exts(root: str | Path, stem: str, exts: Sequence[str]) -> str | None:
root = str(root)
for ext in exts:
path = os.path.join(root, stem + ext)
if os.path.exists(path):
return path
return None
def read_caption(path: str, default: str = "") -> str:
if not path or not os.path.exists(path):
return default
with open(path, "r", encoding="utf-8", errors="ignore") as f:
text = f.read().strip()
return text or default
def _resize_crop_1ch(x: np.ndarray, target_size: int, mode: str) -> torch.Tensor:
x_t = torch.from_numpy(x.astype(np.float32)).unsqueeze(0).unsqueeze(0)
h, w = x_t.shape[-2:]
short = min(h, w)
scale = float(target_size) / float(short)
new_h, new_w = int(round(h * scale)), int(round(w * scale))
x_t = F.interpolate(x_t, size=(new_h, new_w), mode=mode, align_corners=False if mode == "bilinear" else None)
top = (new_h - target_size) // 2
left = (new_w - target_size) // 2
return x_t[:, :, top:top + target_size, left:left + target_size].squeeze(0)
def load_depth_to_tensor(path: str, target_size: int, normalize: bool = True, invert_depth: bool = False) -> torch.Tensor:
ext = os.path.splitext(path)[1].lower()
if ext == ".npy":
depth = np.load(path).astype(np.float32)
elif ext == ".npz":
archive = np.load(path)
depth = archive[list(archive.keys())[0]].astype(np.float32)
else:
with Image.open(path) as im:
im = im.convert("I") if im.mode in ("I", "I;16") else im.convert("L")
depth = np.asarray(im, dtype=np.float32)
if depth.ndim == 3:
depth = depth.mean(axis=-1)
out = _resize_crop_1ch(depth, target_size, mode="bilinear")
if normalize:
lo, hi = out.min(), out.max()
out = (out - lo) / (hi - lo).clamp_min(1e-6)
if invert_depth:
out = 1.0 - out
return out.clamp_(0.0, 1.0)
def load_seg_to_tensor(path: str, target_size: int, normalize: bool = True) -> torch.Tensor:
ext = os.path.splitext(path)[1].lower()
if ext == ".npy":
seg = np.load(path)
else:
with Image.open(path) as im:
seg = np.asarray(im.convert("L"))
if seg.ndim == 3:
seg = seg[..., 0]
out = _resize_crop_1ch(seg.astype(np.float32), target_size, mode="nearest")
if normalize:
max_id = out.max()
if max_id.item() > 0:
out = out / max_id
out = out.clamp_(0.0, 1.0)
return out
def load_edge_to_tensor(path: str, target_size: int) -> torch.Tensor:
ext = os.path.splitext(path)[1].lower()
if ext == ".npy":
edge = np.load(path).astype(np.float32)
else:
with Image.open(path) as im:
edge = np.asarray(im.convert("L"), dtype=np.float32)
if edge.ndim == 3:
edge = edge.mean(axis=-1)
out = _resize_crop_1ch(edge, target_size, mode="bilinear")
lo, hi = out.min(), out.max()
out = (out - lo) / (hi - lo).clamp_min(1e-6)
return out.clamp_(0.0, 1.0)
def subdir_range(start: int, end: int) -> list[str]:
return [f"sa_{i:06d}" for i in range(int(start), int(end) + 1)]
class PixelThreeControlDataset(Dataset):
"""Paired RGB/caption/depth/seg/edge dataset.
Returns a dict ready for a PixelDiT-like training loop. The loop can sample
active modes and zero inactive channels using `apply_multi_control_mode`.
"""
def __init__(
self,
image_root: str,
depth_root: str,
seg_root: str,
edge_root: str,
resolution: int = 512,
subdirs: Optional[Sequence[str]] = None,
cache_index_path: str | None = None,
max_retries: int = 20,
seg_normalize: bool = True,
require_caption: bool = True,
):
self.image_root = image_root
self.depth_root = depth_root
self.seg_root = seg_root
self.edge_root = edge_root
self.resolution = int(resolution)
self.subdirs = list(subdirs) if subdirs is not None else None
self.max_retries = int(max_retries)
self.seg_normalize = bool(seg_normalize)
self.require_caption = bool(require_caption)
self.samples: list[dict] = []
if cache_index_path and os.path.exists(cache_index_path):
self.samples = json.load(open(cache_index_path, "r", encoding="utf-8"))
else:
self._build_index()
if cache_index_path:
Path(cache_index_path).parent.mkdir(parents=True, exist_ok=True)
json.dump(self.samples, open(cache_index_path, "w", encoding="utf-8"))
self.resize = Resize(self.resolution)
self.center_crop = CenterCrop(self.resolution)
self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
def _iter_subdirs(self):
if self.subdirs is not None:
return self.subdirs
return sorted(p.name for p in Path(self.image_root).iterdir() if p.is_dir())
def _build_index(self):
for sub in self._iter_subdirs():
image_dir = Path(self.image_root) / sub
depth_dir = Path(self.depth_root) / sub
seg_dir = Path(self.seg_root) / sub
edge_dir = Path(self.edge_root) / sub
if not image_dir.is_dir() or not depth_dir.is_dir() or not seg_dir.is_dir() or not edge_dir.is_dir():
continue
for cap in sorted(image_dir.glob("*.txt")):
stem = cap.stem
image_path = find_with_exts(image_dir, stem, IMAGE_EXTS)
depth_path = find_with_exts(depth_dir, stem, DEPTH_EXTS)
seg_path = find_with_exts(seg_dir, stem, SEG_EXTS)
edge_path = find_with_exts(edge_dir, stem, EDGE_EXTS)
if image_path and depth_path and seg_path and edge_path:
self.samples.append(
{
"stem": stem,
"image_path": image_path,
"caption_path": str(cap),
"depth_path": depth_path,
"seg_path": seg_path,
"edge_path": edge_path,
}
)
def __len__(self):
return len(self.samples)
def _build_item(self, idx: int):
sample = self.samples[idx]
pil = Image.open(sample["image_path"]).convert("RGB")
pil = self.center_crop(self.resize(pil))
image_01 = to_tensor(pil)
image_m11 = self.normalize(image_01)
depth = load_depth_to_tensor(sample["depth_path"], self.resolution)
seg = load_seg_to_tensor(sample["seg_path"], self.resolution, normalize=self.seg_normalize)
edge = load_edge_to_tensor(sample["edge_path"], self.resolution)
control = torch.cat([depth, seg, edge], dim=0)
return {
"image": image_m11,
"caption": read_caption(sample["caption_path"]),
"control": control,
"control_keep": torch.tensor([1.0, 1.0, 1.0], dtype=torch.float32),
"control_mode": "depth_seg_edge",
"depth": depth,
"seg": seg,
"edge": edge,
**sample,
}
def __getitem__(self, idx: int):
cur = int(idx)
for _ in range(self.max_retries):
try:
return self._build_item(cur)
except Exception as exc:
nxt = random.randint(0, len(self.samples) - 1)
print(f"[PixelThreeControlDataset] bad sample idx={cur}: {exc!r}; retry idx={nxt}")
cur = nxt
raise RuntimeError(f"failed to load valid sample after {self.max_retries} retries")
class PixelSingleControlDataset(Dataset):
"""Single-control depth/seg/edge dataset for baseline training."""
def __init__(
self,
image_root: str,
control_root: str,
control_type: str,
resolution: int = 512,
subdirs: Optional[Sequence[str]] = None,
seg_normalize: bool = True,
):
if control_type not in {"depth", "seg", "edge"}:
raise ValueError("control_type must be depth, seg, or edge")
self.image_root = image_root
self.control_root = control_root
self.control_type = control_type
self.resolution = int(resolution)
self.subdirs = list(subdirs) if subdirs is not None else None
self.seg_normalize = bool(seg_normalize)
self.samples: list[dict] = []
self._build_index()
self.resize = Resize(self.resolution)
self.center_crop = CenterCrop(self.resolution)
self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
def _iter_subdirs(self):
if self.subdirs is not None:
return self.subdirs
return sorted(p.name for p in Path(self.image_root).iterdir() if p.is_dir())
def _find_control(self, control_dir: Path, stem: str):
if self.control_type == "depth":
return find_with_exts(control_dir, stem, DEPTH_EXTS)
if self.control_type == "seg":
return find_with_exts(control_dir, stem, SEG_EXTS)
return find_with_exts(control_dir, stem, EDGE_EXTS)
def _build_index(self):
for sub in self._iter_subdirs():
image_dir = Path(self.image_root) / sub
control_dir = Path(self.control_root) / sub
if not image_dir.is_dir() or not control_dir.is_dir():
continue
for cap in sorted(image_dir.glob("*.txt")):
stem = cap.stem
image_path = find_with_exts(image_dir, stem, IMAGE_EXTS)
control_path = self._find_control(control_dir, stem)
if image_path and control_path:
self.samples.append(
{"stem": stem, "image_path": image_path, "caption_path": str(cap), "control_path": control_path}
)
def __len__(self):
return len(self.samples)
def _load_control(self, path: str):
if self.control_type == "depth":
return load_depth_to_tensor(path, self.resolution)
if self.control_type == "seg":
return load_seg_to_tensor(path, self.resolution, normalize=self.seg_normalize)
return load_edge_to_tensor(path, self.resolution)
def __getitem__(self, idx: int):
sample = self.samples[int(idx)]
pil = Image.open(sample["image_path"]).convert("RGB")
pil = self.center_crop(self.resize(pil))
image_m11 = self.normalize(to_tensor(pil))
control = self._load_control(sample["control_path"])
return {
"image": image_m11,
"caption": read_caption(sample["caption_path"]),
"control": control,
"control_keep": torch.tensor([1.0], dtype=torch.float32),
"control_mode": self.control_type,
self.control_type: control,
**sample,
}
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